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Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis

Nicolás Rubido, Venia Batziou, Marwan Fuad, Vesna Vuksanovic Orcid Logo

arXiv

Swansea University Authors: Venia Batziou, Vesna Vuksanovic Orcid Logo

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DOI (Published version): 10.48550/arXiv.2505.03458

Abstract

Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal inf...

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URI: https://cronfa.swan.ac.uk/Record/cronfa69526
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spelling 2026-03-03T15:19:56.1376251 v2 69526 2025-05-16 Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis 35a4b3e1c71f435e8dfdf2a35f442eaa Venia Batziou Venia Batziou true false a1a6e2bd0b6ee99f648abb6201dea474 0000-0003-4655-698X Vesna Vuksanovic Vesna Vuksanovic true false 2025-05-16 Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum. Working paper arXiv 0 0 0 0001-01-01 10.48550/arXiv.2505.03458 Preprint article before certification by peer review. COLLEGE NANME COLLEGE CODE Swansea University 2026-03-03T15:19:56.1376251 2025-05-16T10:50:20.4610432 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Nicolás Rubido 1 Venia Batziou 2 Marwan Fuad 3 Vesna Vuksanovic 0000-0003-4655-698X 4 69526__36265__eac7f41761db4696aea946d5da4bd39c.pdf Dynamic_Brain_Networks_in_AD_and_MCI_Plus_RF_SciRep_R1_vv.pdf 2026-02-18T14:23:21.9269900 Output 7540034 application/pdf Pre-print true Preprint article before certification by peer review. Released under the terms of a CC-BY license. true eng
title Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
spellingShingle Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
Venia Batziou
Vesna Vuksanovic
title_short Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
title_full Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
title_fullStr Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
title_full_unstemmed Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
title_sort Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis
author_id_str_mv 35a4b3e1c71f435e8dfdf2a35f442eaa
a1a6e2bd0b6ee99f648abb6201dea474
author_id_fullname_str_mv 35a4b3e1c71f435e8dfdf2a35f442eaa_***_Venia Batziou
a1a6e2bd0b6ee99f648abb6201dea474_***_Vesna Vuksanovic
author Venia Batziou
Vesna Vuksanovic
author2 Nicolás Rubido
Venia Batziou
Marwan Fuad
Vesna Vuksanovic
format Working paper
container_title arXiv
institution Swansea University
doi_str_mv 10.48550/arXiv.2505.03458
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
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description Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum.
published_date 0001-01-01T06:54:12Z
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